Package org.apache.spark.ml

DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

See: Description

Interface Summary

Interface	Description
MLEvent	Event emitted by ML operations.
MLEvents	A small trait that defines some methods to send MLEvent.
PredictorParams	(private[ml]) Trait for parameters for prediction (regression and classification).

Class Summary

Class	Description
Estimator<M extends Model<M>>	:: DeveloperApi :: Abstract class for estimators that fit models to data.
FitEnd<M extends Model<M>>	Event fired after Estimator.fit.
FitStart<M extends Model<M>>	Event fired before Estimator.fit.
LoadInstanceEnd<T>	Event fired after MLReader.load.
LoadInstanceStart<T>	Event fired before MLReader.load.
Model<M extends Model<M>>	:: DeveloperApi :: A fitted model, i.e., a Transformer produced by an Estimator.
Pipeline	A simple pipeline, which acts as an estimator.
Pipeline.SharedReadWrite$	Methods for MLReader and MLWriter shared between Pipeline and PipelineModel
PipelineModel	Represents a fitted pipeline.
PipelineStage	:: DeveloperApi :: A stage in a pipeline, either an Estimator or a Transformer.
PredictionModel<FeaturesType,M extends PredictionModel<FeaturesType,M>>	:: DeveloperApi :: Abstraction for a model for prediction tasks (regression and classification).
Predictor<FeaturesType,Learner extends Predictor<FeaturesType,Learner,M>,M extends PredictionModel<FeaturesType,M>>	:: DeveloperApi :: Abstraction for prediction problems (regression and classification).
SaveInstanceEnd	Event fired after MLWriter.save.
SaveInstanceStart	Event fired before MLWriter.save.
TransformEnd	Event fired after Transformer.transform.
Transformer	:: DeveloperApi :: Abstract class for transformers that transform one dataset into another.
TransformStart	Event fired before Transformer.transform.
UnaryTransformer<IN,OUT,T extends UnaryTransformer<IN,OUT,T>>	:: DeveloperApi :: Abstract class for transformers that take one input column, apply transformation, and output the result as a new column.

Package org.apache.spark.ml Description

DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.